The goal of this dissertation is to test the relative strengths of individual and community variables in predicting recidivism. Previous research shows that individual variables can account to predict recidivism to some degree. However, does the community in which an ex-prisoner lives and works have a measurable effect on the likelihood of recidivism as well? The first models test for interaction effects between concentrated disadvantage and race. Results show that race strongly predicts recidivism (blacks being much more likely to recidivate than whites). This remains the case in spite of multiple controls accounting for racial differences, and concentrated disadvantage has no effect on recidivism. Second, models use hierarchical logistic regression to identify if individual variable effects are mediated by the degree of concentrated disadvantage in the community they return to. Results show that concentrated disadvantage does not change the effects of individual-level variables. Third, event history analyses are used to build life tables demonstrating recidivism rates for each race and sex group over time. Group difference do not manifest until well after release for this cohort, with recidivism rates remaining low until well after release.